Vercel AI SDK

Use vercelAI() when your app calls generateText, streamText, or an AI SDK agent. The integration supports AI SDK v7 and v6. It creates one Lemma trace for each AI SDK run, puts the current user turn on the root, promotes threadId / userId from telemetry metadata, and records model calls and tool executions as child spans. Create a new vercelAI() integration for each AI SDK operation. One integration object owns one in-flight run; concurrent reuse fails fast because AI SDK terminal events do not carry a reliable run ID. Sequential reuse after a completed run is supported.

Install

npm install @uselemma/tracing ai zod

AI SDK v7

Pass a fresh Lemma telemetry integration to the AI SDK telemetry.integrations option. Use telemetry.functionId for the agent name and put conversation identity in telemetry.metadata.

import { generateText, tool } from "ai";
import { z } from "zod";
import { vercelAI } from "@uselemma/tracing";

const lemmaTelemetry = vercelAI({
  apiKey: process.env.LEMMA_API_KEY,
  projectId: process.env.LEMMA_PROJECT_ID,
  release: process.env.LEMMA_RELEASE,
});

try {
  const result = await generateText({
    model,
    prompt: userMessage,
    tools: {
      searchDocs: tool({
        inputSchema: z.object({
          query: z.string(),
        }),
        execute: async ({ query }) => searchDocs(query),
      }),
    },
    telemetry: {
      functionId: "support-agent",
      metadata: {
        threadId: conversationId,
        userId: user.id,
      },
      integrations: [lemmaTelemetry],
    },
  });
  await lemmaTelemetry.flush();
  return result.text;
} catch (error) {
  await lemmaTelemetry.fail(error);
  throw error;
}

AI SDK v6

For AI SDK v6, pass the same helper through experimental_telemetry.integrations. Use experimental_telemetry.functionId for the agent name.

import { generateText, tool } from "ai";
import { z } from "zod";
import { vercelAI } from "@uselemma/tracing";

const lemmaTelemetry = vercelAI({
  apiKey: process.env.LEMMA_API_KEY,
  projectId: process.env.LEMMA_PROJECT_ID,
  release: process.env.LEMMA_RELEASE,
});

try {
  const result = await generateText({
    model,
    prompt: userMessage,
    tools: {
      searchDocs: tool({
        inputSchema: z.object({
          query: z.string(),
        }),
        execute: async ({ query }) => searchDocs(query),
      }),
    },
    experimental_telemetry: {
      functionId: "support-agent",
      metadata: {
        threadId: conversationId,
        userId: user.id,
      },
      integrations: [lemmaTelemetry],
    },
  });
  await lemmaTelemetry.flush();
  return result.text;
} catch (error) {
  await lemmaTelemetry.fail(error);
  throw error;
}

Streaming

Use the same per-operation integration with streamText. Call flush() after the stream finishes to send the trace. Call fail(error) if the call throws before a terminal callback.

import { streamText } from "ai";
import { vercelAI } from "@uselemma/tracing";

const lemmaTelemetry = vercelAI({
  apiKey: process.env.LEMMA_API_KEY,
  projectId: process.env.LEMMA_PROJECT_ID,
  release: process.env.LEMMA_RELEASE,
});

const result = streamText({
  model,
  prompt: userMessage,
  telemetry: {
    functionId: "support-agent",
    metadata: {
      threadId: conversationId,
      userId: user.id,
    },
    integrations: [lemmaTelemetry],
  },
});

for await (const _part of result.fullStream) {
  // stream to your response
}

await lemmaTelemetry.flush();

What Lemma records

AI SDK event	Lemma record
Run start / finish	Root trace with current-turn input, final answer or root error, `thread_id` / `user_id`, and real wall-clock bounds
Model call / step	Generation with model, provider, normalized messages, structured assistant output, and duration
Tool execution	Tool call with name, arguments, result or soft/hard error (never both success output and error), and duration

AI SDK v7 provides model and tool durations directly. AI SDK v6 provides tool execution durations; model-call durations are inferred from start and finish timestamps when the AI SDK does not provide an explicit duration. For v6, system + prompt are normalized into chat messages on the generation. Use telemetry.functionId / experimental_telemetry.functionId for the agent name, or set it on the integration with vercelAI({ agentName: "support-agent" }). Override metadata keys when needed:

vercelAI({
  threadIdKey: "conversationId",
  userIdKey: "customerId",
})

For advanced cases, you can still attach to an existing trace by passing vercelAI({ trace }) or by calling AI SDK inside a lemma.trace() callback. If you use the callback form of lemma.trace(), the callback owns trace closure. If you pass a trace handle to vercelAI({ trace }), the integration closes it automatically at the end of the AI SDK operation. Prompts, tool inputs, tool outputs, model output, and error messages are always recorded. Redact secrets and sensitive user data before they reach the AI SDK call.

Abort, flush, and shutdown

Method	When to use
`fail(error)`	The AI SDK call threw or was aborted before a terminal telemetry callback
`flush()`	Send the integration’s completed traces and await ingest delivery
`shutdown()`	Flush and clear state in workers / serverless teardown

const lemmaTelemetry = vercelAI();

try {
  const result = await generateText({
    model,
    prompt: userMessage,
    telemetry: {
      functionId: "support-agent",
      integrations: [lemmaTelemetry],
    },
  });
  await lemmaTelemetry.flush();
  return result.text;
} catch (error) {
  await lemmaTelemetry.fail(error);
  throw error;
} finally {
  await lemmaTelemetry.shutdown();
}

Getting Started

Instrumentation

Integrations

Connections

Vercel AI SDK

Install

AI SDK v7

AI SDK v6

Streaming

What Lemma records

Abort, flush, and shutdown

​Install

​AI SDK v7

​AI SDK v6

​Streaming

​What Lemma records

​Abort, flush, and shutdown

Install

AI SDK v7

AI SDK v6

Streaming

What Lemma records

Abort, flush, and shutdown